scholarly journals The Impact of the Contribution Micro-environment on Data Quality: The Case of OSM

10.5334/bbf.h ◽  
2017 ◽  
pp. 165-196
Keyword(s):  
Data Quality ◽  
The Impact

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Kazimierz M. Slomczynski ◽  
Irina Tomescu-Dubrow ◽  
Ilona Wysmulek
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Survey Data ◽  
A Priori ◽  
Data Sets ◽  
New Approach ◽  
Survey Quality ◽  
Survey Error ◽  
Ex Post ◽  
The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

scholarly journals Conducting Population Health Research during the COVID-19 Pandemic: Impacts and Recommendations

2021 ◽  
Vol 13 (6) ◽  
pp. 3320
Author(s):  
Amy R. Villarosa ◽  
Lucie M. Ramjan ◽  
Della Maneze ◽  
Ajesh George
Keyword(s):  
Data Collection ◽  
Health Services ◽  
Data Quality ◽  
Population Health ◽  
Health Research ◽  
Alternative Methods ◽  
Residential Aged Care ◽  
Recruitment Methods ◽  
Face To Face ◽  
The Impact

The COVID-19 pandemic has resulted in many changes, including restrictions on indoor gatherings and visitation to residential aged care facilities, hospitals and certain communities. Coupled with potential restrictions imposed by health services and academic institutions, these changes may significantly impact the conduct of population health research. However, the continuance of population health research is beneficial for the provision of health services and sometimes imperative. This paper discusses the impact of COVID-19 restrictions on the conduct of population health research. This discussion unveils important ethical considerations, as well as potential impacts on recruitment methods, face-to-face data collection, data quality and validity. In addition, this paper explores potential recruitment and data collection methods that could replace face-to-face methods. The discussion is accompanied by reflections on the challenges experienced by the authors in their own research at an oral health service during the COVID-19 pandemic and alternative methods that were utilised in place of face-to-face methods. This paper concludes that, although COVID-19 presents challenges to the conduct of population health research, there is a range of alternative methods to face-to-face recruitment and data collection. These alternative methods should be considered in light of project aims to ensure data quality is not compromised.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals The Impact of “No Opinion” Response Options on Data Quality

2001 ◽  
Vol 66 (3) ◽  
pp. 371-403 ◽  
Author(s):  
Jon A. Krosnick ◽  
Allyson L. Holbrook ◽  
Matthew K. Berent ◽  
Richard T. Carson ◽  
W. Michael Hanemann ◽  
...  
Keyword(s):  
Data Quality ◽  
Response Options ◽  
The Impact ◽  
Opinion Response

Between the Spreadsheets

2021 ◽  
Author(s):  
Susan Walsh
Keyword(s):  
Data Quality ◽  
Deep Understanding ◽  
Data Classification ◽  
Dirty Data ◽  
Quality Issues ◽  
Book Covers ◽  
Level Of Experience ◽  
The Impact

Dirty data is a problem that costs businesses thousands, if not millions, every year. In organisations large and small across the globe you will hear talk of data quality issues. What you will rarely hear about is the consequences or how to fix it.<br><br><i>Between the Spreadsheets: Classifying and Fixing Dirty Data</i> draws on classification expert Susan Walsh's decade of experience in data classification to present a fool-proof method for cleaning and classifying your data. The book covers everything from the very basics of data classification to normalisation, taxonomies and presents the author's proven <b>COAT</b> methodology, helping ensure an organisation's data is <b>Consistent</b>, <b>Organised</b>, <b>Accurate</b> and <b>Trustworthy</b>. A series of data horror stories outlines what can go wrong in managing data, and if it does, how it can be fixed. <br><br>After reading this book, regardless of your level of experience, not only will you be able to work with your data more efficiently, but you will also understand the impact the work you do with it has, and how it affects the rest of the organisation.<br><br>Written in an engaging and highly practical manner, <i>Between the Spreadsheets</i> gives readers of all levels a deep understanding of the dangers of dirty data and the confidence and skills to work more efficiently and effectively with it.

Ontology for Data Quality and Chronic Disease Management

2015 ◽  
pp. 303-331
Author(s):  
Alireza Rahimi ◽  
Siaw-Teng Liaw ◽  
Pradeep Kumar Ray ◽  
Jane Taggart ◽  
Hairong Yu
Keyword(s):  
Chronic Disease ◽  
Data Quality ◽  
English Language ◽  
Assessment Tools ◽  
Fitness For Purpose ◽  
Language Studies ◽  
Full Text Review ◽  
Automated Tools ◽  
The Impact

Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontology-based approaches to conceptualization and specification of DQ based on “fitness for purpose” within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers; excluded 36 duplicates, 182 on abstract review, and 46 on full-text review; leaving 52 papers. These were appraised with a realist “context-mechanism-impacts/outcomes” template. The authors found a lack of consensus frameworks or definitions for DQ and comprehensive ontological approaches to DQ or fitness for purpose. The majority of papers described the processes of the development of DQ tools. Some assessed the impact of implementing ontology-based specifications for DQ. There were few evaluative studies of the performance of DQ assessment tools developed; none compared ontological with non-ontological approaches.

scholarly journals Completing Self-administered Questionnaires: Hmong Older Adults and Their Family Helpers

Field Methods ◽  
2019 ◽  
Vol 32 (3) ◽  
pp. 253-273
Author(s):  
Maichou Lor ◽  
Nora Cate Schaeffer ◽  
Roger L. Brown ◽  
Barbara J. Bowers
Keyword(s):  
Older Adults ◽  
Data Quality ◽  
Future Research ◽  
Computer Assisted ◽  
Video Recordings ◽  
English Speaking ◽  
Interview Instrument ◽  
Audio Computer ◽  
The Impact ◽  
Answering Questions

This study describes a method for collecting data from nonliterate, non-English-speaking populations. Our audio computer-assisted self-interview instrument with color-labeled response categories was designed for use with helper assistance. The study included 30 dyads of nonliterate older Hmong respondents and family helpers answering questions about health. Analysis of video recordings identified respondents’ problems and helpers’ strategies to address these problems. Seven dyads displayed the paradigmatic question–answer sequence for all items, while 23 departed from the paradigmatic sequence at least once. Reports and pauses were the most common signs of problems displayed by respondents. Paraphrasing questions or response categories and providing examples were the most common helper strategies. Future research could assess the impact of helpers’ strategies on data quality.

scholarly journals High-Quality UAV-Based Orthophotos for Cadastral Mapping: Guidance for Optimal Flight Configurations

2020 ◽  
Vol 12 (21) ◽  
pp. 3625
Author(s):  
Claudia Stöcker ◽  
Francesco Nex ◽  
Mila Koeva ◽  
Markus Gerke
Keyword(s):  
Data Quality ◽  
Geospatial Information ◽  
Ground Control Points ◽  
Bundle Block Adjustment ◽  
High Resolution Imagery ◽  
Decision Making Processes ◽  
Combination Of Methods ◽  
Study Sites ◽  
The Impact ◽  
Base Data

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

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